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CSOLNP: Numerical Optimization Engine for Solving Non-linearly Constrained Problems.

Mahsa Zahery1, Hermine H Maes2, Michael C Neale2

  • 1Department of Computer Science,Virginia Commonwealth University,Richmond,Virginia,USA.

Twin Research and Human Genetics : the Official Journal of the International Society for Twin Studies
|May 25, 2017
PubMed
Summary
This summary is machine-generated.

CSOLNP, a new C++ optimizer, offers faster performance than NPSOL and SLSQP for ordinal data analysis. It also matches NPSOL

Keywords:
NPSOLOpenMxRSOLNPSLSQPnon-linear programmingsequential quadratic programming

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Area of Science:

  • Computational Statistics
  • Numerical Optimization

Background:

  • Non-linearly constrained optimization problems are common in statistical modeling.
  • Existing optimizers like NPSOL and SLSQP have limitations in performance and memory usage.

Purpose of the Study:

  • Introduce CSOLNP, a C++ implementation of RSOLNP with improvements.
  • Compare the performance of CSOLNP against NPSOL and SLSQP.

Main Methods:

  • CSOLNP utilizes a Sequential Quadratic Programming (SQP) algorithm.
  • Performance was evaluated using Monte Carlo analysis on ordinal and continuous models.
  • Memory consumption was assessed using Valgrind's Massif tool.

Main Results:

  • CSOLNP is generally faster than NPSOL and SLSQP for ordinal analysis, with objective value differences under 0.5%.
  • No consistent speed advantage was found for continuous data.
  • CSOLNP and NPSOL have similar memory consumption, while SLSQP uses 71 MB more.

Conclusions:

  • CSOLNP provides a faster and memory-efficient alternative for ordinal data optimization.
  • The choice of optimizer for continuous data may depend on specific model characteristics.